by 马克孙
脑袋尖尖，我们是伦敦的理工学生，想要做一个分享科技新闻，探索前沿科技和学术论文的播客。
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🇨🇳
Publishing Since
12/30/2024
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December 30, 2024
<p>概述本期主题：</p><p>深入解析 Scaling Law及其在模型性能优化中的应用。</p><p>核心问题：</p><p>Scaling Law 描述了模型性能如何随着模型参数规模、数据集大小和计算资源投入增长，展现出一定的幂律关系。</p><p>讨论内容涵盖 Scaling Law 的核心公式、应用场景，潜在局限性及未来发展的可能性。</p><p>时间轴</p><p>00:00 - 开场与节目介绍</p><p>01:02 - 什么是 Scaling Law？核心概念解析</p><p>11:48 - Scaling Law 的现实意义及限制</p><p>19:51 - 应用案例：OpenAI 的研究成果及行业观察</p><p>23:16 - Scaling Law 的限制及未来发展方向</p><p>24:16 - 结尾总结</p><p>相关资源:</p><p>OpenAI 的 Scaling Law 研究论文</p><p>https://doi.org/10.48550/arXiv.2001.08361</p><p>Hestness, Joel; Narang, Sharan; Ardalani, Newsha; Diamos, Gregory; Jun, Heewoo; Kianinejad, Hassan; Patwary, Md Mostofa Ali; Yang, Yang; Zhou, Yanqi (2017-12-01). "Deep Learning Scaling is Predictable, Empirically"</p><p>https://doi.org/10.48550/arXiv.1712.00409</p><p>Hoffmann, Jordan; Borgeaud, Sebastian; Mensch, Arthur; Buchatskaya, Elena; Cai, Trevor; Rutherford, Eliza; Casas, Diego de Las; Hendricks, Lisa Anne; Welbl, Johannes; Clark, Aidan; Hennigan, Tom; Noland, Eric; Millican, Katie; Driessche, George van den; Damoc, Bogdan (2022-03-29). "Training Compute-Optimal Large Language Models"</p><p>https://doi.org/10.48550/arXiv.2203.15556</p><p>联系我们</p><p>如果喜欢本期节目，请订阅、分享，欢迎留下宝贵的意见，如有错误请多指正，新人上路，请多指教！</p><p>联系方式：公众号【孙霄逸 Xiaoyi】</p><p>特别鸣谢:</p><p>本期主播：马克孙， 老柏</p><p>感谢团队：对本期节目的策划与剪辑支持。</p>
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